Temperature monitoring during radiofrequency ablation of liver: in vivo trials

Current and future technologies for monitoring cultured meat: A review

The high population growth rate, massive animal food consumption, fast economic progress, and limited food resources could lead to a food crisis in the future. There is a huge requirement for dietary proteins including cultured meat is being progressed to fulfill the need for meat-derived proteins in the diet. However, production of cultured meat requires monitoring numerous bioprocess parameters. This review presents a comprehensive overview of various widely adopted techniques (optical, spectroscopic, electrochemical, capacitive, FETs, resistive, microscopy, and ultrasound) for monitoring physical, chemical, and biological parameters that can improve the bioprocess control in cultured meat. The methods, operating principle, merits/demerits, and the main open challenges are reviewed with the aim to support the readers in advancing knowledge on novel sensing systems for cultured meat applications.

Non-invasive mass and temperature quantifications with spectral CT

Spectral CT has been increasingly implemented clinically for its better characterization and quantification of materials through its multi-energy results. It also facilitates calculation of physical density, allowing for non-invasive mass measurements and temperature evaluations by manipulating the definition of physical density and thermal volumetric expansion, respectively. To develop spectral physical density quantifications, original and parametrized Alvarez-Macovski model and electron density-physical density model were validated with a phantom. The best physical density model was then implemented on clinical spectral CT scans of ex vivo bovine muscle to determine the accuracy and effect of acquisition parameters on mass measurements. In addition, the relationship between physical density and changes in temperature was evaluated by scanning and subjecting the tissue to a range of temperatures. The parametrized Alvarez-Macovski model performed best in both model development and validation with errors within ± 0.02 g/mL. No effect from acquisition parameters was observed in mass measurements, which demonstrated accuracy with a maximum percent error of 0.34%. Furthermore, physical density was strongly correlated (R of 0.9781) to temperature changes through thermal volumetric expansion. Accurate and precise spectral physical density quantifications enable non-invasive mass measurements for pathological detection and temperature evaluation for thermal therapy monitoring in interventional oncology.

Photon-counting computed tomography thermometry via material decomposition and machine learning

Thermal ablation procedures, such as high intensity focused ultrasound and radiofrequency ablation, are often used to eliminate tumors by minimally invasively heating a focal region. For this task, real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings. Current computed tomography (CT) thermometry is based on energy-integrated CT, tissue-specific experimental data, and linear relationships between attenuation and temperature. In this paper, we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest. In our feasibility study, distilled water, 50 mmol/L CaCl₂, and 600 mmol/L CaCl₂ are chosen as the base materials. Their attenuations are measured in four discrete energy bins at various temperatures. The neural network trained on the experimental data achieves a mean absolute error of 3.97 °C and 1.80 °C on 300 mmol/L CaCl₂ and a milk-based protein shake respectively. These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dissimilar to our base materials.

High Precision Monitoring of Radiofrequency Ablation for Liver Using Hyperspectral Imaging

Minimally invasive procedures are achieving better satisfaction for treating liver cancers. Energy-based techniques were studied as prospective alternatives to the gold standard of liver transplantation. Among these techniques, radiofrequency (RF) was investigated for the selective ablation of liver tissue. In addition to optimizing the RF settings for the purpose of overcoming tissue perforation or inadequate ablation, an instrument collecting quantitative data regarding the intraoperative tissue status can aid the treatment procedure. This study demonstrates an innovative noninvasive technique using hyperspectral imaging (HSI) for monitoring RF ablative therapy in ex-vivo liver tissue. The cubic data generated by HSI provides spectral as well as spatial properties of the liver tissue included in each pixel of the field of view. In our study, the applied statistical analysis saves the computational burdens of multivariate analysis techniques. For this purpose, spectral angle mapper, logistic regression algorithm, and principal component analysis were applied. Of all spectral bands captured by the HSI camera, bands centered at 760 and 960 nm were identified for predicting the ablated area. Based on statistical analysis, the threshold for predicting the ablated area of the liver samples was determined, provided that the specificity is kept at 90%.

PDE-Based Modeling and Non-collocated Feedback Control of Electrosurgical-Probe/Tissue Interaction

The first control-oriented model of the interaction of an electrosurgical probe with organic tissue, based on a 1-D PDE with a moving boundary, is introduced. To attain the desired electrosurgically-induced tissue changes using this model, a non-collocated output feedback moving boundary control law is proposed. The latter is realized through a novel non-collocated pointwise temperature-based state observer for the two-phase Stefan problem. Simulation demonstrates that the controller proposed meets the performance objective. The controller implementation is also discussed.